Within scenario-based research of social-ecological systems, there has been a growing recognition of the importance of normative scenarios that define positive outcomes for both nature and society. While several frameworks exist to guide the co-creation of normative scenario narratives, examples of operationalizing these narratives in quantitative simulation modelling are still limited. To address this gap, this paper presents an example of how aspects of normative scenarios can be realized within a spatial model of land use and land cover change. This is achieved through a combination of data-driven approaches to encapsulate scenario-specific differences in local and global scale phenomena, as well as iterative expert elicitation to quantify descriptive trends from narratives. This approach is demonstrated with a case study simulating five scenarios of landscape change (three normative and two exploratory) in Switzerland between 2020 and 2060. The resulting maps of future land use and land cover exhibited distinct variations between the scenarios, notably with regard to the prevalence of areas of heterogeneous semi-natural land, such as alpine pastures and grassland, often considered culturally emblematic of Switzerland. While the simulation results were generally consistent with the outcomes expressed in the scenario narratives, following a process of expert feedback, we reflect that there are clear challenges in leveraging such results to elicit further discussions as to the desirability and plausibility of future scenarios. Specifically, the need to summarize spatial simulations in a manner that is easily interpretable and encourages consideration of the broader patterns of change rather than focusing on fine-scale details.

Climate projections for continental Europe indicate drier summers, increased annual precipitation, and less snowy winters, which are expected to cause shifts in species' distributions. Yet, most regions/countries currently lack comprehensive climate-driven biodiversity projections across taxonomic groups, challenging effective conservation efforts. To address this gap, our study evaluated the potential effects of climate change on the biodiversity of an alpine country of Europe, Switzerland. We used a state-of-the art species distribution modeling approach and species occurrence data that covered the climatic conditions encountered across the full species' ranges to help limiting niche truncation. We quantified the relationship between baseline climate and the spatial distribution of 7291 species from 12 main taxonomic groups and projected future climate suitability for three 30-year periods and two greenhouse gas concentration scenarios (RCP4.5 and 8.5). Our results indicated important effects of projected climate changes on species' climate suitability, with responses varying by the taxonomic and conservation status group. The percentage of species facing major changes in climate suitability was higher under RCP8.5 (68%) compared to RCP4.5 (66%). By the end of the century, decreases in climate suitability were projected for 3000 species under RCP8.5 and 1758 species under RCP4.5. The most affected groups under RCP8.5 were molluscs, algae, and amphibians, while it was molluscs, birds, and vascular plants under RCP4.5. Spatially, by 2070-2099, we projected an overall decrease in climate suitability for 39% of the cells in the study area under RCP8.5 and 10% under RCP4.5, while projecting an increase for 50% of the cells under RCP8.5 and 73% under RCP4.5. The most consistent geographical shifts were upward, southward, and eastward. We found that the coverage of high climate suitability cells by protected areas was expected to increase. Our models and maps provide guidance for spatial conservation planning by pointing out future climate-suitable areas for biodiversity.

Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.

Changes in climate and land use represent significant risks of biodiversity loss globally, affect ecological stability, impact nature’s contributions to people (NCP, i.e. ecosystem services) and compromise human livelihood. As framings of conservation evolve to consider the interdependence between species and human needs, there is a growing recognition of the importance of NCP and biodiversity in conservation actions. However, knowledge on the interactions and spatial repartition of NCP and biodiversity remains limited. Here we show a comprehensive spatial assessment for 15 NCP and one biodiversity – distribution of threatened species – indicators in Switzerland. Indicators values were computed using a panel of mapping and modelling methods extracted from the literature, or specifically developed for this study. Through the analysis of their relationships, we reveal significant trade-offs and synergies in the spatial repartition of these indicators. Results from a spatial bundle analysis performed on the 16 indicators revealed the existence of four bundles showing a heterogeneous repartition over the Swiss landscape. Furthermore, we identified that topography (slope), climate (temperature and precipitations), and habitat (forest and meadows) were among the most influential factors to explain the spatial distribution of the four bundles. We conclude that various significant relationships exist between NCP and biodiversity indicators in Switzerland, emphasizing the importance of informed conservation approaches considering both NCP and biodiversity supply. This work helps fill the gap in our understanding of the links among different NCP, between NCP and biodiversity, and highlight their relationship to climate and land use, providing key insights for optimizing conservation efforts.

The land use and cover category (SWECO25 v1.0.0) contains the "geostat25" and "wslhabmap" datasets. The geostat25 dataset describes the land use and cover of Switzerland. After resampling the “Downscaled Land Use/Land Cover of Switzerland” source data (Giuliani et al., 2022) to the SWECO25 grid, we generated individual layers for the 65 land use and cover classes and the 3 time periods (1992-1997, 2004-2009, and 2013-2018) that were available. For each class and period, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 2,730 layers. Final values were rounded and multiplied by 100. The wslhabmap dataset (land use and cover category) describes the natural habitats of Switzerland. After rasterizing and resampling the “Habitat Map of Switzerland v1” source data (Price et al., 2021) to the SWECO25 grid, we generated individual layers for 41 categories (32 classes and 9 groups). The groups correspond to the first level of the TypoCH classification and the classes to the second level. For details on the TypoCH classification see Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Guide des milieux naturels de Suisse : Écologie, menaces, espèces caractéristiques. Rossolis. For each of the 41 categories, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given category in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 574 layers. Final values were rounded and multiplied by 100. The detailed list of layers available is provided in SWECO25_datalayers_details_lulc.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path. References: G. Giuliani, D. Rodila, N. Külling, R. Maggini, A. Lehmann, Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System. Land 11, 615 (2022). B. Price, Huber, N., Ginzler, C., Pazúr, R., Rüetschi, M., "The Habitat Map of Switzerland v1," (Birmensdorf, Switzerland, 2021)

The remote-sensing indices category (SWECO25 v1.0.0) contains the "sdc" dataset. The sdc dataset (remote-sensing indices category) provides data on vegetation indices in Switzerland. After reprojecting and resampling data from the Swiss Data cube (Chatenoux et al., 2021) to the SWECO25 grid, we generated mean and standard deviation layers for five indices (NDVI, NDWI, LAI, GCI, and EVI) at a yearly time step for the 1996-2021 period, when available. This dataset includes a total of 243 layers. Final values were rounded and multiplied by 100. However, for layers that were initially multiplied by 1000 (LAI, GCI, EVI), they were divided by 10 instead. The detailed list of layers available is provided in SWECO25_datalayers_details_rs.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path. References: Chatenoux, B. et al. The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Scientific data 8, 295 (2021).

The geologic category (SWECO25 v1.0.0) contains the "geotechnic" dataset. The geotechnic dataset describes the subsoil of Switzerland. After rasterizing and resampling the source data (Swisstopo, 1967) to the SWECO25 grid, we generated individual layers for the 30 classes that were available. For each class, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 420 layers. Final values were rounded and multiplied by 100. The detailed list of layers available is provided in SWECO25_datalayers_details_geol.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path. Reference: Swiss Federal Office of Topography [swisstopo]. Geotechnical map of Switzerland. (Wabern, Switzerland, 1967)

The hydrologic category (SWECO25 v1.0.0) contains the "gwn07", "morph", and "swisstopo" datasets. The gwn07 dataset provides information on the distance to the hydrological network (rivers and lakes). After rasterizing and resampling the source data (Swisstopo, 2007) to the SWECO25 grid, we generated distance statistics layers for the 9 (and all together) Strahler River order and 3 (and all together) lake size classes that were available. This dataset includes a total of 14 layers. Final values were rounded and multiplied by 100. The morph dataset describes the ecomorphology of the Swiss rivers and streams. After rasterizing and resampling the source data (FOEN, 2009) to the SWECO25 grid, we generated individual layers for the 5 ecomorphological classes that were available (natural/near-natural, little disturbed, heavily disturbed, unnatural/artificial, and culverted). For each class, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 71 layers. Final values were rounded and multiplied by 100. The swisstopo dataset describes the steepness of the watercourses. After rasterizing and resampling the source data (Kaelin and Altermatt, 2016) to the SWECO25 grid, we generated two main layers, for the maximum and mean values. For each, we provided the output map and 13 focal statistics layers obtained by applying a cell-level function calculating the average value in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 28 layers. Final values were rounded and multiplied by 100. The detailed list of layers available is provided in SWECO25_datalayers_details_hydro.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path. References: Swiss Federal Office of Topography [swisstopo]. Hydrographic network VECTOR25 GWN07. (Wabern, Switzerland, 2007). Federal Office for the Environment [FOEN]. Swiss watercourse structure and morphology. (Bern, Switzerland, 2009) Kaelin, K. & Altermatt, F. Landscape-level predictions of diversity in river networks reveal opposing patterns for different groups of macroinvertebrates. Aquatic Ecology 50, 283-295 (2016)

The edaphic category (SWECO25 v1.0.0) contains the "eiv" and "modiffus" datasets. The eiv dataset includes variables representing local soil properties and climate conditions. After resampling the source data (Descombes et al., 2020) to the SWECO25 grid, we generated individual layers for the 8 available variables (soil pH, nutrients, moisture, moisture variability, aeration, humus, climate continentality, and light). For each variable, we provided a layer with the raw values and 13 focal statistics layers by applying a cell-level function calculating the mean value in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 112 layers. Final values were rounded and multiplied by 100. The modiffus dataset describes the nitrogen (n) and phosphorus (p) loads in Swiss soils. After resampling the source data (Hürdler et al., 2015) to the SWECO25 grid for these two variables, we provided the output maps and computed 13 focal statistics layers by applying a cell-level function calculating the average value in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 28 layers. Final values were rounded and multiplied by 100. The detailed list of layers available is provided in SWECO25_datalayers_details_edaph.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path. References: Descombes, P. et al. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography 43, 1448-1463 (2020). Hürdler J., P. V., Spiess E. Abschätzung diffuser Stickstoff- und Phosphoreinträge in die Gewässer der Schweiz MODIFFUS 3.0: Bericht im Auftrag des Bundesamtes für Umwelt (BAFU). (Zürich, Switzerland, 2015).

High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and calculation of indices. Downscaled LU/LC maps for Switzerland were obtained for three time periods by blending two inputs: the Swiss topographic base map at a 1:25,000 scale and the national LU/LC statistics obtained from aerial photointerpretation on a 100 m regular lattice of points. The spatial resolution of the resulting LU/LC map was improved by a factor of 16 to reach a resolution of 25 m, while the thematic resolution was increased from 29 (in the base map) to 62 land use categories. The method combines a simple inverse distance spatial weighting of 36 nearest neighbors' information and an expert system of correspondence between input base map categories and possible output LU/LC types. The developed algorithm, written in Python, reads and writes gridded layers of more than 64 million pixels. Given the size of the analyzed area, a High-Performance Computing (HPC) cluster was used to parallelize the data and the analysis and to obtain results more efficiently. The method presented in this study is a generalizable approach that can be used to downscale different types of geographic information.